Similarity-Based Classification in Relational Databases

Article type: Research Article

Authors: Hońko, Piotr

Affiliations: Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland. e-mail: p.honko@pb.edu.pl

Note: [] Address for correspondence: Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351, Białystok, Poland

Abstract: In this paper, we introduce a method for measuring similarity of objects of a relational database (relational objects, in short). We also propose and investigate an algorithm SC for classification of relational objects. The task of classification is carried out based on similarity of the objects to predefined classes. An object to be classified is assigned to the class to which it is most similar. A similarity of an object to a class is understood as its similarity to a class representative. Several methods for computing the class representative are proposed. We test the algorithm on real and artificial databases. We compare results obtained by the algorithm with those obtained by other algorithms known from the literature. We also present our approach in the context of granular computing.

Keywords: multi-relational data mining, classification, similarity measures, granular computing

DOI: 10.3233/FI-2010-284

Journal: Fundamenta Informaticae, vol. 101, no. 3, pp. 187-213, 2010